Businesses may leverage transaction data from numerous data sources all over the world to obtain business performance metrics, such as product performance, profitability, margins, and other critical metrics, and consumer behavior information. Such business performance metrics and consumer behavior information may in turn be used to optimize business operations and improve profits. Each data source may generate a large volume of data that need to be processed in a timely manner to benefit from the data. However, businesses may lack the data processing power, capacity and infrastructure required to address the large volume of data. Upgrading existing data processing infrastructure to include additional processing power may be time consuming and cost-intensive. Further, even if the large volume of data can be handled, the data from the data sources may be incompatible with data systems associated with the businesses, and business may need complex conversion mechanisms to convert the data from each data source to a form that is understandable by the data systems of the business for analysis.
In addition, even if the business can efficiently process the large volumes of the data and convert the data effectively, conventional technology may lack ability to provide the data in near real-time. Instead, conventional technology may be equipped to provide the data in a batch fashion by the next day, within a week, within a month, or even by end of a business quarter. Said limitations of the conventional technology may in turn limit the analytical and data mining capabilities of businesses to leverage the data in a timely manner to benefit from the data. For example, businesses may have to wait for a month before they can obtain a product performance analysis and in a highly competitive environment, by the time they receive the analysis the product performance trends may have changed. Such limitations may limit the ability of businesses to foresee trends and take necessary proactive measure to maximize benefits and minimize losses. Thus, there is a need for a technology that provides real-time transaction data processing and reporting.